Bioaccessibility, digestibility and nutritional properties of algae and Cyanophyceae as basis of their potential as functional food ingredients

Peñalver, Rocío; Lorenzo, Jose Manuel; Nieto, Gema

doi:10.1016/j.afres.2024.100404

Applied Food Research

2024

DOI: 10.1016/j.afres.2024.100404

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Bioaccessibility, digestibility and nutritional properties of algae and Cyanophyceae as basis of their potential as functional food ingredients

Rocío Peñalver,

Jose Manuel Lorenzo,

Gema Nieto

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Cited by 4 publications

References 90 publications

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Hypoglycemic peptide preparation from Bacillus subtilis fermented with Pyropia: Identification, molecular docking, and in vivo confirmation

Han,

Xu,

et al. 2025

Food Chemistry

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Hypoglycemic peptide preparation from Bacillus subtilis fermented with Pyropia: Identification, molecular docking, and in vivo confirmation

Han,

Xu,

et al. 2025

Food Chemistry

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Effects of the consumption of algal biomass versus protein concentrate on postprandial satiety and metabolism

Wu,

Tso,

Yong

et al. 2024

Future Foods

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Evaluation of Nutritional Values of Edible Algal Species Using a Shortwave Infrared Hyperspectral Imaging and Machine Learning Technique

Amoriello,

Mellara,

Amoriello

et al. 2024

Foods

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In recent years, the growing demand for algae in Western countries is due to their richness in nutrients and bioactive compounds, and their use as ingredients for foods, cosmetics, nutraceuticals, fertilizers, biofuels,, etc. Evaluation of the qualitative characteristics of algae involves assessing their physicochemical and nutritional components to determine their suitability for specific end uses, but this assessment is generally performed using destructive, expensive, and time-consuming traditional chemical analyses, and requires sample preparation. The hyperspectral imaging (HSI) technique has been successfully applied in food quality assessment and control and has the potential to overcome the limitations of traditional biochemical methods. In this study, the nutritional profile (proteins, lipids, and fibers) of seventeen edible macro- and microalgae species widely grown throughout the world were investigated using traditional methods. Moreover, a shortwave infrared (SWIR) hyperspectral imaging device and artificial neural network (ANN) algorithms were used to develop multi-species models for proteins, lipids, and fibers. The predictive power of the models was characterized by different metrics, which showed very high predictive performances for all nutritional parameters (for example, R2 = 0.9952, 0.9767, 0.9828 for proteins, lipids, and fibers, respectively). Our results demonstrated the ability of SWIR hyperspectral imaging coupled with ANN algorithms in quantifying biomolecules in algal species in a fast and sustainable way.

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Bioaccessibility, digestibility and nutritional properties of algae and Cyanophyceae as basis of their potential as functional food ingredients

Cited by 4 publications

References 90 publications

Hypoglycemic peptide preparation from Bacillus subtilis fermented with Pyropia: Identification, molecular docking, and in vivo confirmation

Hypoglycemic peptide preparation from Bacillus subtilis fermented with Pyropia: Identification, molecular docking, and in vivo confirmation

Effects of the consumption of algal biomass versus protein concentrate on postprandial satiety and metabolism

Evaluation of Nutritional Values of Edible Algal Species Using a Shortwave Infrared Hyperspectral Imaging and Machine Learning Technique

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